{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "86c7c89b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2.60\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tushare as ts\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(ts.__version__)\n",
    "pro = ts.pro_api('ea3263c5424f08c3e04d605af9458cfc349613d5b7c27e99994eb396')\n",
    "\n",
    "holdings={\n",
    "    \"格力电器\":\"000651.sz\",\n",
    "    \"伊利股份\":\"600887.sh\",\n",
    "    \"中国银行\":\"601988.sh\",\n",
    "    \"光大银行\":\"601818.sh\",\n",
    "    \"万科A\":\"000002.sz\",\n",
    "    \"东方财富\":\"300059.sz\",\n",
    "    \"乐普医疗\":\"300003.sz\",\n",
    "    \"奕东电子\":\"301123.sz\"\n",
    "}\n",
    "#多个股票\n",
    "#df = pro.daily(ts_code='000001.SZ,600000.SH', start_date='20180701', end_date='20180718')\n",
    "def get_daily_data(ts_code, start, end):\n",
    "    return pro.daily(ts_code=ts_code, start_date=start, end_date=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4ff837f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "start='20220101'\n",
    "end='20220429'\n",
    "def output_to_excel():\n",
    "    writer=pd.ExcelWriter('holdings.xlsx', mode='a')\n",
    "    for k in holdings.keys():\n",
    "        print(k)\n",
    "        df=get_daily_data(holdings[k], start, end)\n",
    "        df.to_excel(writer,sheet_name=k,index=False)\n",
    "    writer.save()\n",
    "#df=get_daily_data(\"格力电器\", start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1868b112",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000651.sz,600887.sh,601988.sh,601818.sh,000002.sz,300059.sz,300003.sz,301123.sz\n"
     ]
    }
   ],
   "source": [
    "ts_code=\",\".join(holdings.values())\n",
    "print(ts_code)\n",
    "df=get_daily_data(ts_code,start,end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e3801268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>18.75</td>\n",
       "      <td>19.77</td>\n",
       "      <td>18.39</td>\n",
       "      <td>19.38</td>\n",
       "      <td>18.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.6364</td>\n",
       "      <td>1917457.47</td>\n",
       "      <td>3.665755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000651.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>30.36</td>\n",
       "      <td>31.33</td>\n",
       "      <td>30.30</td>\n",
       "      <td>31.21</td>\n",
       "      <td>30.31</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.9693</td>\n",
       "      <td>420627.22</td>\n",
       "      <td>1.299788e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300003.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>16.38</td>\n",
       "      <td>17.15</td>\n",
       "      <td>16.30</td>\n",
       "      <td>17.06</td>\n",
       "      <td>16.06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.2267</td>\n",
       "      <td>241056.49</td>\n",
       "      <td>4.058629e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300059.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>21.65</td>\n",
       "      <td>23.04</td>\n",
       "      <td>21.10</td>\n",
       "      <td>22.78</td>\n",
       "      <td>21.05</td>\n",
       "      <td>1.73</td>\n",
       "      <td>8.2185</td>\n",
       "      <td>5044525.77</td>\n",
       "      <td>1.124298e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>301123.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>21.27</td>\n",
       "      <td>22.58</td>\n",
       "      <td>21.01</td>\n",
       "      <td>22.32</td>\n",
       "      <td>20.88</td>\n",
       "      <td>1.44</td>\n",
       "      <td>6.8966</td>\n",
       "      <td>41638.53</td>\n",
       "      <td>9.179751e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>300003.SZ</td>\n",
       "      <td>20220104</td>\n",
       "      <td>22.70</td>\n",
       "      <td>23.78</td>\n",
       "      <td>22.64</td>\n",
       "      <td>23.62</td>\n",
       "      <td>22.63</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.3747</td>\n",
       "      <td>388156.84</td>\n",
       "      <td>9.024742e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>300059.SZ</td>\n",
       "      <td>20220104</td>\n",
       "      <td>37.25</td>\n",
       "      <td>37.28</td>\n",
       "      <td>35.75</td>\n",
       "      <td>35.86</td>\n",
       "      <td>37.11</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-3.3684</td>\n",
       "      <td>1388634.04</td>\n",
       "      <td>5.017007e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>600887.SH</td>\n",
       "      <td>20220104</td>\n",
       "      <td>41.05</td>\n",
       "      <td>41.40</td>\n",
       "      <td>40.30</td>\n",
       "      <td>40.70</td>\n",
       "      <td>41.46</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-1.8331</td>\n",
       "      <td>567676.38</td>\n",
       "      <td>2.312303e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>601818.SH</td>\n",
       "      <td>20220104</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.3012</td>\n",
       "      <td>1203937.02</td>\n",
       "      <td>3.998082e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>601988.SH</td>\n",
       "      <td>20220104</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.6557</td>\n",
       "      <td>1138933.19</td>\n",
       "      <td>3.485263e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>601 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0    000002.SZ   20220429  18.75  19.77  18.39  19.38      18.70    0.68   \n",
       "1    000651.SZ   20220429  30.36  31.33  30.30  31.21      30.31    0.90   \n",
       "2    300003.SZ   20220429  16.38  17.15  16.30  17.06      16.06    1.00   \n",
       "3    300059.SZ   20220429  21.65  23.04  21.10  22.78      21.05    1.73   \n",
       "4    301123.SZ   20220429  21.27  22.58  21.01  22.32      20.88    1.44   \n",
       "..         ...        ...    ...    ...    ...    ...        ...     ...   \n",
       "596  300003.SZ   20220104  22.70  23.78  22.64  23.62      22.63    0.99   \n",
       "597  300059.SZ   20220104  37.25  37.28  35.75  35.86      37.11   -1.25   \n",
       "598  600887.SH   20220104  41.05  41.40  40.30  40.70      41.46   -0.76   \n",
       "599  601818.SH   20220104   3.32   3.33   3.31   3.33       3.32    0.01   \n",
       "600  601988.SH   20220104   3.06   3.07   3.05   3.07       3.05    0.02   \n",
       "\n",
       "     pct_chg         vol        amount  \n",
       "0     3.6364  1917457.47  3.665755e+06  \n",
       "1     2.9693   420627.22  1.299788e+06  \n",
       "2     6.2267   241056.49  4.058629e+05  \n",
       "3     8.2185  5044525.77  1.124298e+07  \n",
       "4     6.8966    41638.53  9.179751e+04  \n",
       "..       ...         ...           ...  \n",
       "596   4.3747   388156.84  9.024742e+05  \n",
       "597  -3.3684  1388634.04  5.017007e+06  \n",
       "598  -1.8331   567676.38  2.312303e+06  \n",
       "599   0.3012  1203937.02  3.998082e+05  \n",
       "600   0.6557  1138933.19  3.485263e+05  \n",
       "\n",
       "[601 rows x 11 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a2dca1e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "writer=pd.ExcelWriter('holdings.xlsx', mode='a')\n",
    "df.to_excel(writer,sheet_name=\"全部\",index=False)\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b8e4b324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndef plot_multi_stocks(df,ts_code, axs=None):\\n    drawer=plt if axs is None else axs\\n    for key in ts_code:\\n        print(key)\\n        drawer.plot(df[key].close)\\n    #drawer.plot(wk, c='g')\\n    #drawer.grid(True)\\n    plt.legend(ts_code,loc=1)\\nplot_multi_stocks(df.close,holdings.values())\\nplt.xlabel('time')\\nplt.ylabel('close')\\nplt.title('Zhongguo Pingan and Wanke')\\n\""
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "def plot_multi_stocks(df,ts_code, axs=None):\n",
    "    drawer=plt if axs is None else axs\n",
    "    for key in ts_code:\n",
    "        print(key)\n",
    "        drawer.plot(df[key].close)\n",
    "    #drawer.plot(wk, c='g')\n",
    "    #drawer.grid(True)\n",
    "    plt.legend(ts_code,loc=1)\n",
    "plot_multi_stocks(df.close,holdings.values())\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('close')\n",
    "plt.title('Zhongguo Pingan and Wanke')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "375cbe76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('000002.SZ', '20220429', 18.75, 19.77, 18.39, 19.38,  18.7, ...),\n",
       "            ('000651.SZ', '20220429', 30.36, 31.33,  30.3, 31.21, 30.31, ...),\n",
       "            ('300003.SZ', '20220429', 16.38, 17.15,  16.3, 17.06, 16.06, ...),\n",
       "            ('300059.SZ', '20220429', 21.65, 23.04,  21.1, 22.78, 21.05, ...),\n",
       "            ('301123.SZ', '20220429', 21.27, 22.58, 21.01, 22.32, 20.88, ...),\n",
       "            ('600887.SH', '20220429', 38.06, 38.68, 37.75, 38.47, 38.06, ...),\n",
       "            ('601818.SH', '20220429',   3.2,   3.2,  3.16,  3.19,  3.19, ...),\n",
       "            ('601988.SH', '20220429',  3.26,  3.27,  3.21,  3.23,  3.26, ...),\n",
       "            ('000002.SZ', '20220428', 18.21, 19.03, 18.05,  18.7, 18.23, ...),\n",
       "            ('000651.SZ', '20220428',  30.4, 30.64,  29.9, 30.31, 30.46, ...),\n",
       "            ...\n",
       "            ('600887.SH', '20220105',  40.6, 42.18, 40.31,  41.7,  40.7, ...),\n",
       "            ('601818.SH', '20220105',  3.33,  3.37,  3.32,  3.36,  3.33, ...),\n",
       "            ('601988.SH', '20220105',  3.07,   3.1,  3.06,  3.09,  3.07, ...),\n",
       "            ('000002.SZ', '20220104', 19.49, 20.65, 19.36, 20.49, 19.76, ...),\n",
       "            ('000651.SZ', '20220104', 37.03, 37.75, 36.84, 37.58, 37.03, ...),\n",
       "            ('300003.SZ', '20220104',  22.7, 23.78, 22.64, 23.62, 22.63, ...),\n",
       "            ('300059.SZ', '20220104', 37.25, 37.28, 35.75, 35.86, 37.11, ...),\n",
       "            ('600887.SH', '20220104', 41.05,  41.4,  40.3,  40.7, 41.46, ...),\n",
       "            ('601818.SH', '20220104',  3.32,  3.33,  3.31,  3.33,  3.32, ...),\n",
       "            ('601988.SH', '20220104',  3.06,  3.07,  3.05,  3.07,  3.05, ...)],\n",
       "           names=['ts_code', 'trade_date', 'open', 'high', 'low', 'close', 'pre_close', 'change', 'pct_chg', 'vol', 'amount'], length=601)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.query['000002.sz'].close()\n",
    "df.ts_code.drop_duplicates()\n",
    "index=pd.MultiIndex.from_product([df.ts_code.drop_duplicates()])\n",
    "#index=[df.trade_date.drop_duplicates(),df.ts_code.drop_duplicates()]\n",
    "pd.MultiIndex.from_frame(df)\n",
    "#df['ts_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1a0e3fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.DataFrameGroupBy"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group=df.groupby(by=['ts_code'])\n",
    "type(group)\n",
    "#group['000002.SZ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "22845612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>18.75</td>\n",
       "      <td>19.77</td>\n",
       "      <td>18.39</td>\n",
       "      <td>19.38</td>\n",
       "      <td>18.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.6364</td>\n",
       "      <td>1917457.47</td>\n",
       "      <td>3665754.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220428</td>\n",
       "      <td>18.21</td>\n",
       "      <td>19.03</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.70</td>\n",
       "      <td>18.23</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2.5782</td>\n",
       "      <td>1592032.23</td>\n",
       "      <td>2947417.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220427</td>\n",
       "      <td>18.66</td>\n",
       "      <td>19.21</td>\n",
       "      <td>17.72</td>\n",
       "      <td>18.23</td>\n",
       "      <td>18.96</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-3.8502</td>\n",
       "      <td>1844961.26</td>\n",
       "      <td>3368085.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220426</td>\n",
       "      <td>18.80</td>\n",
       "      <td>19.55</td>\n",
       "      <td>18.65</td>\n",
       "      <td>18.96</td>\n",
       "      <td>18.83</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.6904</td>\n",
       "      <td>1061400.57</td>\n",
       "      <td>2023745.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220425</td>\n",
       "      <td>19.60</td>\n",
       "      <td>19.99</td>\n",
       "      <td>18.80</td>\n",
       "      <td>18.83</td>\n",
       "      <td>19.81</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-4.9470</td>\n",
       "      <td>1127800.75</td>\n",
       "      <td>2172308.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220110</td>\n",
       "      <td>21.80</td>\n",
       "      <td>22.66</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.16</td>\n",
       "      <td>21.89</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.2334</td>\n",
       "      <td>1527796.63</td>\n",
       "      <td>3376585.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220107</td>\n",
       "      <td>21.38</td>\n",
       "      <td>22.46</td>\n",
       "      <td>21.35</td>\n",
       "      <td>21.89</td>\n",
       "      <td>21.05</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.9905</td>\n",
       "      <td>2262768.05</td>\n",
       "      <td>4969780.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220106</td>\n",
       "      <td>21.12</td>\n",
       "      <td>21.66</td>\n",
       "      <td>20.79</td>\n",
       "      <td>21.05</td>\n",
       "      <td>21.17</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.5668</td>\n",
       "      <td>1380274.06</td>\n",
       "      <td>2923511.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220105</td>\n",
       "      <td>20.43</td>\n",
       "      <td>21.61</td>\n",
       "      <td>20.36</td>\n",
       "      <td>21.17</td>\n",
       "      <td>20.49</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.3187</td>\n",
       "      <td>1930457.91</td>\n",
       "      <td>4075047.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20220104</td>\n",
       "      <td>19.49</td>\n",
       "      <td>20.65</td>\n",
       "      <td>19.36</td>\n",
       "      <td>20.49</td>\n",
       "      <td>19.76</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.6943</td>\n",
       "      <td>1947202.02</td>\n",
       "      <td>3956475.666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0    000002.SZ   20220429  18.75  19.77  18.39  19.38      18.70    0.68   \n",
       "8    000002.SZ   20220428  18.21  19.03  18.05  18.70      18.23    0.47   \n",
       "16   000002.SZ   20220427  18.66  19.21  17.72  18.23      18.96   -0.73   \n",
       "24   000002.SZ   20220426  18.80  19.55  18.65  18.96      18.83    0.13   \n",
       "32   000002.SZ   20220425  19.60  19.99  18.80  18.83      19.81   -0.98   \n",
       "..         ...        ...    ...    ...    ...    ...        ...     ...   \n",
       "566  000002.SZ   20220110  21.80  22.66  21.56  22.16      21.89    0.27   \n",
       "573  000002.SZ   20220107  21.38  22.46  21.35  21.89      21.05    0.84   \n",
       "580  000002.SZ   20220106  21.12  21.66  20.79  21.05      21.17   -0.12   \n",
       "587  000002.SZ   20220105  20.43  21.61  20.36  21.17      20.49    0.68   \n",
       "594  000002.SZ   20220104  19.49  20.65  19.36  20.49      19.76    0.73   \n",
       "\n",
       "     pct_chg         vol       amount  \n",
       "0     3.6364  1917457.47  3665754.967  \n",
       "8     2.5782  1592032.23  2947417.800  \n",
       "16   -3.8502  1844961.26  3368085.084  \n",
       "24    0.6904  1061400.57  2023745.887  \n",
       "32   -4.9470  1127800.75  2172308.177  \n",
       "..       ...         ...          ...  \n",
       "566   1.2334  1527796.63  3376585.470  \n",
       "573   3.9905  2262768.05  4969780.143  \n",
       "580  -0.5668  1380274.06  2923511.291  \n",
       "587   3.3187  1930457.91  4075047.844  \n",
       "594   3.6943  1947202.02  3956475.666  \n",
       "\n",
       "[77 rows x 11 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取符合条件的行\n",
    "tdf=df[(df['ts_code'] == '000002.SZ')].copy()\n",
    "tdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ce4113e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('information_schema',), ('stock',)]\n"
     ]
    }
   ],
   "source": [
    " import mysql.connector\n",
    "conn=mysql.connector.connect(host=\"192.168.1.29\",user=\"stock\",password=\"Holdings!23\")\n",
    "cur=conn.cursor()\n",
    "cur.execute(\"show databases\")\n",
    "db=cur.fetchall()\n",
    "print(db)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "29ee8141",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "hostname=\"192.168.1.29\"\n",
    "db=\"stock\"\n",
    "username=\"stock\"\n",
    "password=\"Holdings!23\"\n",
    "\n",
    "\n",
    "\n",
    "# Create SQLAlchemy engine to connect to MySQL Database\n",
    "engine = create_engine(\"mysql+pymysql://{user}:{password}@{host}/{database}\".\n",
    "                       format(host=hostname, database=db, user=username, password=password))\n",
    "\n",
    "# Convert dataframe to sql table                                   \n",
    "#df.to_sql('stock_daily', engine, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "162590d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#z-socre规范化，regular_std\n",
    "def get_regular_std(df,ts_code):\n",
    "    tdf=df[(df['ts_code'] == ts_code)].copy()\n",
    "    rstd_close = (tdf['close'] -tdf['close'].mean()) / tdf['close'].std() \n",
    "    tdf['close']=rstd_close\n",
    "    rstd_open = (tdf['open'] -tdf['open'].mean()) / tdf['open'].std()\n",
    "    tdf['open']=rstd_open\n",
    "    rstd_high = (tdf['high'] -tdf['high'].mean()) / tdf['high'].std() \n",
    "    tdf['high']=rstd_high\n",
    "    rstd_high = (tdf['low'] -tdf['low'].mean()) / tdf['low'].std() \n",
    "    tdf['low']=rstd_high\n",
    "    return tdf\n",
    "#ts_df.to_sql('regular_std_stock_daily', engine,if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3dbfedcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,ts_code in enumerate(holdings.values()):\n",
    "    ts_df=get_regular_std(df, ts_code.upper())\n",
    "    if i==0:\n",
    "        ts_df.to_sql('regular_std_stock_daily', engine, if_exists='replace', index=False)\n",
    "        \n",
    "    else:\n",
    "        ts_df.to_sql('regular_std_stock_daily',  engine, if_exists='append', index=False)\n",
    "            \n",
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f028c3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 000651.sz\n",
      "1 600887.sh\n",
      "2 601988.sh\n",
      "3 601818.sh\n",
      "4 000002.sz\n",
      "5 300059.sz\n",
      "6 300003.sz\n",
      "7 301123.sz\n"
     ]
    }
   ],
   "source": [
    "def get_regular_mm(df, ts_code):\n",
    "    # 最小-最大规范化，对原始序列进行变换\n",
    "    tdf=df[(df['ts_code'] == ts_code)].copy()\n",
    "    rmm_close = (tdf['close'] -tdf['close'].min()) / (tdf['close'].max()-tdf['close'].min())\n",
    "    tdf['close']=rmm_close\n",
    "    rmm_open = (tdf['open'] -tdf['open'].min()) / (tdf['open'].max()-tdf['open'].min())\n",
    "    tdf['open']=rmm_open\n",
    "    rmm_high = (tdf['high'] -tdf['high'].min()) / (tdf['high'].max()-tdf['high'].min())\n",
    "    tdf['high']=rmm_high\n",
    "    rmm_low = (tdf['low'] -tdf['low'].min()) / (tdf['low'].max()-tdf['low'].min())\n",
    "    tdf['low']=rmm_low\n",
    "    return tdf\n",
    "\n",
    "for i,ts_code in enumerate(holdings.values()):\n",
    "    ts_df=get_regular_mm(df, ts_code.upper())\n",
    "    print(i, ts_code)\n",
    "    if i==0:\n",
    "        ts_df.to_sql('regular_mm_stock_daily', engine, if_exists='replace', index=False)  \n",
    "    else:\n",
    "        ts_df.to_sql('regular_mm_stock_daily',  engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "99a0fb0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000001.SZ,300957.SZ,603605.SH,601166.SH\n"
     ]
    }
   ],
   "source": [
    "holdings={\n",
    "    \"平安银行\":\"000001.SZ\",\n",
    "    \"备泰妮\":\"300957.SZ\",\n",
    "    \"珀莱雅\":\"603605.SH\",\n",
    "    \"兴业银行\":\"601166.SH\"\n",
    "}\n",
    "ts_code=\",\".join(holdings.values())\n",
    "print(ts_code)\n",
    "start=\"20200101\"\n",
    "df=get_daily_data(\",\".join(holdings.values()),start,end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f4f1ef6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>15.69</td>\n",
       "      <td>15.74</td>\n",
       "      <td>14.97</td>\n",
       "      <td>15.32</td>\n",
       "      <td>15.65</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-2.1086</td>\n",
       "      <td>1645013.08</td>\n",
       "      <td>2503476.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300957.SZ</td>\n",
       "      <td>20220429</td>\n",
       "      <td>198.00</td>\n",
       "      <td>205.75</td>\n",
       "      <td>196.59</td>\n",
       "      <td>196.90</td>\n",
       "      <td>197.30</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.2027</td>\n",
       "      <td>20904.51</td>\n",
       "      <td>419741.593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>601166.SH</td>\n",
       "      <td>20220429</td>\n",
       "      <td>20.95</td>\n",
       "      <td>20.95</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.48</td>\n",
       "      <td>21.05</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-2.7078</td>\n",
       "      <td>1827993.88</td>\n",
       "      <td>3712928.953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>603605.SH</td>\n",
       "      <td>20220429</td>\n",
       "      <td>191.76</td>\n",
       "      <td>203.99</td>\n",
       "      <td>190.62</td>\n",
       "      <td>201.46</td>\n",
       "      <td>195.67</td>\n",
       "      <td>5.79</td>\n",
       "      <td>2.9591</td>\n",
       "      <td>16954.52</td>\n",
       "      <td>339254.514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date    open    high     low   close  pre_close  change  \\\n",
       "0  000001.SZ   20220429   15.69   15.74   14.97   15.32      15.65   -0.33   \n",
       "1  300957.SZ   20220429  198.00  205.75  196.59  196.90     197.30   -0.40   \n",
       "2  601166.SH   20220429   20.95   20.95   20.00   20.48      21.05   -0.57   \n",
       "3  603605.SH   20220429  191.76  203.99  190.62  201.46     195.67    5.79   \n",
       "\n",
       "   pct_chg         vol       amount  \n",
       "0  -2.1086  1645013.08  2503476.827  \n",
       "1  -0.2027    20904.51   419741.593  \n",
       "2  -2.7078  1827993.88  3712928.953  \n",
       "3   2.9591    16954.52   339254.514  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"trade_date==@end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "54fbb88f",
   "metadata": {},
   "outputs": [
    {
     "ename": "OperationalError",
     "evalue": "(pymysql.err.OperationalError) (2013, 'Lost connection to MySQL server during query ([WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。)')\n[SQL: SELECT COUNT(*) FROM information_schema.tables WHERE table_schema = %(table_schema)s AND table_name = %(table_name)s]\n[parameters: {'table_schema': 'stock', 'table_name': 'stock_daily'}]\n(Background on this error at: https://sqlalche.me/e/14/e3q8)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36m_read_bytes\u001b[1;34m(self, num_bytes)\u001b[0m\n\u001b[0;32m    690\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 691\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_bytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    692\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python37\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    588\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    590\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1819\u001b[0m                     self.dialect.do_execute(\n\u001b[1;32m-> 1820\u001b[1;33m                         \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1821\u001b[0m                     )\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[1;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[0;32m    731\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m         \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    733\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, query, args)\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 170\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    171\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\cursors.py\u001b[0m in \u001b[0;36m_query\u001b[1;34m(self, q)\u001b[0m\n\u001b[0;32m    327\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    329\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36mquery\u001b[1;34m(self, sql, unbuffered)\u001b[0m\n\u001b[0;32m    516\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_execute_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCOMMAND\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOM_QUERY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msql\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_affected_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_query_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munbuffered\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munbuffered\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_affected_rows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36m_read_query_result\u001b[1;34m(self, unbuffered)\u001b[0m\n\u001b[0;32m    731\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMySQLResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m             \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    733\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1074\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1075\u001b[1;33m             \u001b[0mfirst_packet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_packet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1076\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36m_read_packet\u001b[1;34m(self, packet_type)\u001b[0m\n\u001b[0;32m    656\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 657\u001b[1;33m             \u001b[0mpacket_header\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    658\u001b[0m             \u001b[1;31m#if DEBUG: dump_packet(packet_header)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36m_read_bytes\u001b[1;34m(self, num_bytes)\u001b[0m\n\u001b[0;32m    698\u001b[0m                     \u001b[0mCR\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCR_SERVER_LOST\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 699\u001b[1;33m                     \"Lost connection to MySQL server during query (%s)\" % (e,))\n\u001b[0m\u001b[0;32m    700\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOperationalError\u001b[0m: (2013, 'Lost connection to MySQL server during query ([WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。)')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_141224/1027010333.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'stock_daily'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mif_exists\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'append'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mts_code\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mholdings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mts_df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_regular_std\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mts_code\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mts_code\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mts_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'regular_std_stock_daily'\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mif_exists\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'append'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python37\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)\u001b[0m\n\u001b[0;32m   2613\u001b[0m             \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2614\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2615\u001b[1;33m             \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2616\u001b[0m         )\n\u001b[0;32m   2617\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python37\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)\u001b[0m\n\u001b[0;32m    596\u001b[0m         \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m         \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m     )\n\u001b[0;32m    600\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python37\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method)\u001b[0m\n\u001b[0;32m   1391\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1392\u001b[0m         )\n\u001b[1;32m-> 1393\u001b[1;33m         \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1395\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0msqlalchemy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python37\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    719\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    720\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 721\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    722\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mif_exists\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"fail\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    723\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Table '{self.name}' already exists.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python37\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mexists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    706\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    707\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 708\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpd_sql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    709\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msql_schema\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files\\python37\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mhas_table\u001b[1;34m(self, name, schema)\u001b[0m\n\u001b[0;32m   1429\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhas_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1430\u001b[0m         return self.connectable.run_callable(\n\u001b[1;32m-> 1431\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_table\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mschema\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1432\u001b[0m         )\n\u001b[0;32m   1433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<string>\u001b[0m in \u001b[0;36mrun_callable\u001b[1;34m(self, callable_, *args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\util\\deprecations.py\u001b[0m in \u001b[0;36mwarned\u001b[1;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    399\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mskip_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    400\u001b[0m             \u001b[0m_warn_with_version\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 401\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    403\u001b[0m     \u001b[0mdoc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36mrun_callable\u001b[1;34m(self, callable_, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3141\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"_sa_skip_warning\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3142\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3143\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcallable_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3145\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_ddl_visitor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvisitorcallable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0melement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<string>\u001b[0m in \u001b[0;36mrun_callable\u001b[1;34m(self, callable_, *args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\util\\deprecations.py\u001b[0m in \u001b[0;36mwarned\u001b[1;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    399\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mskip_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    400\u001b[0m             \u001b[0m_warn_with_version\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 401\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    403\u001b[0m     \u001b[0mdoc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36mrun_callable\u001b[1;34m(self, callable_, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2209\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2210\u001b[0m         \"\"\"\n\u001b[1;32m-> 2211\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mcallable_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\dialects\\mysql\\base.py\u001b[0m in \u001b[0;36mhas_table\u001b[1;34m(self, connection, table_name, schema)\u001b[0m\n\u001b[0;32m   2652\u001b[0m             {\n\u001b[0;32m   2653\u001b[0m                 \u001b[1;34m\"table_schema\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mschema\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2654\u001b[1;33m                 \u001b[1;34m\"table_name\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2655\u001b[0m             },\n\u001b[0;32m   2656\u001b[0m         )\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[0;32m   1304\u001b[0m             )\n\u001b[0;32m   1305\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1306\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_EMPTY_EXECUTION_OPTS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1308\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_execute_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\sql\\elements.py\u001b[0m in \u001b[0;36m_execute_on_connection\u001b[1;34m(self, connection, multiparams, params, execution_options, _force)\u001b[0m\n\u001b[0;32m    324\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0m_force\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupports_execution\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m             return connection._execute_clauseelement(\n\u001b[1;32m--> 326\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    327\u001b[0m             )\n\u001b[0;32m    328\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_clauseelement\u001b[1;34m(self, elem, multiparams, params, execution_options)\u001b[0m\n\u001b[0;32m   1506\u001b[0m             \u001b[0melem\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1507\u001b[0m             \u001b[0mextracted_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1508\u001b[1;33m             \u001b[0mcache_hit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_hit\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1509\u001b[0m         )\n\u001b[0;32m   1510\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhas_events\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1861\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1862\u001b[0m             self._handle_dbapi_exception(\n\u001b[1;32m-> 1863\u001b[1;33m                 \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1864\u001b[0m             )\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[1;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[0;32m   2042\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mshould_wrap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2043\u001b[0m                 util.raise_(\n\u001b[1;32m-> 2044\u001b[1;33m                     \u001b[0msqlalchemy_exception\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrom_\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2045\u001b[0m                 )\n\u001b[0;32m   2046\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\util\\compat.py\u001b[0m in \u001b[0;36mraise_\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m    205\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    206\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 207\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    208\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    209\u001b[0m             \u001b[1;31m# credit to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1818\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1819\u001b[0m                     self.dialect.do_execute(\n\u001b[1;32m-> 1820\u001b[1;33m                         \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1821\u001b[0m                     )\n\u001b[0;32m   1822\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[1;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[0;32m    730\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    731\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m         \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    733\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    734\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, query, args)\u001b[0m\n\u001b[0;32m    168\u001b[0m         \u001b[0mquery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmogrify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 170\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    171\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\cursors.py\u001b[0m in \u001b[0;36m_query\u001b[1;34m(self, q)\u001b[0m\n\u001b[0;32m    326\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_last_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    329\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrowcount\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36mquery\u001b[1;34m(self, sql, unbuffered)\u001b[0m\n\u001b[0;32m    515\u001b[0m                 \u001b[0msql\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'surrogateescape'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_execute_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCOMMAND\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOM_QUERY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msql\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_affected_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_query_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munbuffered\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munbuffered\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_affected_rows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36m_read_query_result\u001b[1;34m(self, unbuffered)\u001b[0m\n\u001b[0;32m    730\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    731\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMySQLResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m             \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    733\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    734\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserver_status\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1073\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1074\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1075\u001b[1;33m             \u001b[0mfirst_packet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_packet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1076\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1077\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mfirst_packet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_ok_packet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36m_read_packet\u001b[1;34m(self, packet_type)\u001b[0m\n\u001b[0;32m    655\u001b[0m         \u001b[0mbuff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mb''\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    656\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 657\u001b[1;33m             \u001b[0mpacket_header\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    658\u001b[0m             \u001b[1;31m#if DEBUG: dump_packet(packet_header)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    659\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pymysql\\connections.py\u001b[0m in \u001b[0;36m_read_bytes\u001b[1;34m(self, num_bytes)\u001b[0m\n\u001b[0;32m    697\u001b[0m                 raise err.OperationalError(\n\u001b[0;32m    698\u001b[0m                     \u001b[0mCR\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCR_SERVER_LOST\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 699\u001b[1;33m                     \"Lost connection to MySQL server during query (%s)\" % (e,))\n\u001b[0m\u001b[0;32m    700\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m                 \u001b[1;31m# Don't convert unknown exception to MySQLError.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOperationalError\u001b[0m: (pymysql.err.OperationalError) (2013, 'Lost connection to MySQL server during query ([WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。)')\n[SQL: SELECT COUNT(*) FROM information_schema.tables WHERE table_schema = %(table_schema)s AND table_name = %(table_name)s]\n[parameters: {'table_schema': 'stock', 'table_name': 'stock_daily'}]\n(Background on this error at: https://sqlalche.me/e/14/e3q8)"
     ]
    }
   ],
   "source": [
    "df.to_sql('stock_daily', engine,if_exists='append', index=False)\n",
    "for i,ts_code in enumerate(holdings.values()):\n",
    "    ts_df=get_regular_std(df, ts_code.upper())\n",
    "    print(i, ts_code)\n",
    "    ts_df.to_sql('regular_std_stock_daily',  engine, if_exists='append', index=False)\n",
    "for i,ts_code in enumerate(holdings.values()):\n",
    "    ts_df=get_regular_mm(df, ts_code.upper())\n",
    "    print(i, ts_code)\n",
    "    ts_df.to_sql('regular_mm_stock_daily',  engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f52ce310",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(563, 11)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
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       "      <th>low</th>\n",
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       "      <th>1954</th>\n",
       "      <td>601166.SH</td>\n",
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       "      <td>20.07</td>\n",
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       "      <td>601166.SH</td>\n",
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       "      <td>20.20</td>\n",
       "      <td>20.30</td>\n",
       "      <td>20.10</td>\n",
       "      <td>20.14</td>\n",
       "      <td>20.21</td>\n",
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       "      <td>601166.SH</td>\n",
       "      <td>20200106</td>\n",
       "      <td>20.06</td>\n",
       "      <td>20.24</td>\n",
       "      <td>19.85</td>\n",
       "      <td>19.91</td>\n",
       "      <td>20.14</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-1.1420</td>\n",
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       "      <td>601166.SH</td>\n",
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       "      <td>20.25</td>\n",
       "      <td>19.86</td>\n",
       "      <td>19.99</td>\n",
       "      <td>19.91</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.4018</td>\n",
       "      <td>561162.55</td>\n",
       "      <td>1124203.504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>601166.SH</td>\n",
       "      <td>20200108</td>\n",
       "      <td>19.85</td>\n",
       "      <td>19.94</td>\n",
       "      <td>19.53</td>\n",
       "      <td>19.58</td>\n",
       "      <td>19.99</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-2.0510</td>\n",
       "      <td>881731.51</td>\n",
       "      <td>1735240.172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>601166.SH</td>\n",
       "      <td>20220425</td>\n",
       "      <td>20.91</td>\n",
       "      <td>21.09</td>\n",
       "      <td>20.25</td>\n",
       "      <td>20.28</td>\n",
       "      <td>21.42</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-5.3221</td>\n",
       "      <td>1492988.43</td>\n",
       "      <td>3086710.294</td>\n",
       "      <td>0.054477</td>\n",
       "      <td>-0.088223</td>\n",
       "      <td>0.142699</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>601166.SH</td>\n",
       "      <td>20220426</td>\n",
       "      <td>20.38</td>\n",
       "      <td>20.74</td>\n",
       "      <td>20.24</td>\n",
       "      <td>20.42</td>\n",
       "      <td>20.28</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.6903</td>\n",
       "      <td>910385.33</td>\n",
       "      <td>1866639.024</td>\n",
       "      <td>-0.015209</td>\n",
       "      <td>-0.126327</td>\n",
       "      <td>0.111117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>601166.SH</td>\n",
       "      <td>20220427</td>\n",
       "      <td>20.26</td>\n",
       "      <td>20.79</td>\n",
       "      <td>20.22</td>\n",
       "      <td>20.54</td>\n",
       "      <td>20.42</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.5877</td>\n",
       "      <td>877748.85</td>\n",
       "      <td>1803283.308</td>\n",
       "      <td>-0.060061</td>\n",
       "      <td>-0.136942</td>\n",
       "      <td>0.076882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>601166.SH</td>\n",
       "      <td>20220428</td>\n",
       "      <td>20.50</td>\n",
       "      <td>21.07</td>\n",
       "      <td>20.46</td>\n",
       "      <td>21.05</td>\n",
       "      <td>20.54</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2.4830</td>\n",
       "      <td>1054012.55</td>\n",
       "      <td>2198281.365</td>\n",
       "      <td>-0.053832</td>\n",
       "      <td>-0.104571</td>\n",
       "      <td>0.050739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>601166.SH</td>\n",
       "      <td>20220429</td>\n",
       "      <td>20.95</td>\n",
       "      <td>20.95</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.48</td>\n",
       "      <td>21.05</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-2.7078</td>\n",
       "      <td>1827993.88</td>\n",
       "      <td>3712928.953</td>\n",
       "      <td>-0.093809</td>\n",
       "      <td>-0.115638</td>\n",
       "      <td>0.021829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>563 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "1954  601166.SH   20200102  20.07  20.42  20.05  20.21      19.80    0.41   \n",
       "1951  601166.SH   20200103  20.20  20.30  20.10  20.14      20.21   -0.07   \n",
       "1948  601166.SH   20200106  20.06  20.24  19.85  19.91      20.14   -0.23   \n",
       "1945  601166.SH   20200107  20.00  20.25  19.86  19.99      19.91    0.08   \n",
       "1942  601166.SH   20200108  19.85  19.94  19.53  19.58      19.99   -0.41   \n",
       "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
       "18    601166.SH   20220425  20.91  21.09  20.25  20.28      21.42   -1.14   \n",
       "14    601166.SH   20220426  20.38  20.74  20.24  20.42      20.28    0.14   \n",
       "10    601166.SH   20220427  20.26  20.79  20.22  20.54      20.42    0.12   \n",
       "6     601166.SH   20220428  20.50  21.07  20.46  21.05      20.54    0.51   \n",
       "2     601166.SH   20220429  20.95  20.95  20.00  20.48      21.05   -0.57   \n",
       "\n",
       "      pct_chg         vol       amount  MACD_12_26_9  MACDh_12_26_9  \\\n",
       "1954   2.0707  1130028.43  2290358.487           NaN            NaN   \n",
       "1951  -0.3464   659291.02  1331182.965           NaN            NaN   \n",
       "1948  -1.1420   709164.86  1423391.729           NaN            NaN   \n",
       "1945   0.4018   561162.55  1124203.504           NaN            NaN   \n",
       "1942  -2.0510   881731.51  1735240.172           NaN            NaN   \n",
       "...       ...         ...          ...           ...            ...   \n",
       "18    -5.3221  1492988.43  3086710.294      0.054477      -0.088223   \n",
       "14     0.6903   910385.33  1866639.024     -0.015209      -0.126327   \n",
       "10     0.5877   877748.85  1803283.308     -0.060061      -0.136942   \n",
       "6      2.4830  1054012.55  2198281.365     -0.053832      -0.104571   \n",
       "2     -2.7078  1827993.88  3712928.953     -0.093809      -0.115638   \n",
       "\n",
       "      MACDs_12_26_9  \n",
       "1954            NaN  \n",
       "1951            NaN  \n",
       "1948            NaN  \n",
       "1945            NaN  \n",
       "1942            NaN  \n",
       "...             ...  \n",
       "18         0.142699  \n",
       "14         0.111117  \n",
       "10         0.076882  \n",
       "6          0.050739  \n",
       "2          0.021829  \n",
       "\n",
       "[563 rows x 14 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_ta as ta\n",
    "# Calculate MACD values using the pandas_ta library\n",
    "data=df[(df.ts_code=='601166.SH')].sort_index(axis=0, ascending=False)\n",
    "print(data.shape)\n",
    "data.ta.macd(close='close', fast=12, slow=26, signal=9, append=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "29b44d9c",
   "metadata": {},
   "outputs": [
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          187.9,
          14.68,
          177.6,
          19.88,
          182.15,
          14.85,
          173.64,
          20.03,
          184.2,
          14.98,
          175.15,
          20.07,
          180.52,
          15.2,
          175.38,
          19.85,
          180.31,
          15,
          169.15,
          19.71,
          178.02,
          15.18,
          156.23,
          20.81,
          174.05,
          14.59,
          156.83,
          20.22,
          177,
          14.7,
          161.19,
          20.76,
          174,
          14.45,
          158,
          19.91,
          172.01,
          14.31,
          155.55,
          19.82,
          162,
          13.68,
          149,
          18.85,
          159.5,
          14.49,
          155.3,
          20.14,
          170.21,
          14.9,
          166.99,
          20.8,
          180.1,
          14.57,
          166.97,
          20.67,
          180.5,
          13.84,
          156.63,
          20.47,
          167.17,
          14.31,
          157.22,
          20.6,
          166.75,
          14.72,
          164.61,
          21.34,
          173.6,
          15.33,
          169.4,
          21.71,
          176.38,
          15.71,
          162.26,
          22.08,
          176.05,
          15.68,
          161.77,
          22.06,
          181.79,
          15.92,
          163.12,
          22.17,
          183.58,
          15.75,
          165.6,
          22.07,
          185.95,
          15.9,
          168.42,
          22.11,
          182.79,
          15.91,
          162.3,
          22.32,
          181.7,
          16.19,
          170.63,
          22.5,
          184.86,
          16.23,
          164.2,
          23.08,
          184,
          16.51,
          167.1,
          22.76,
          185.87,
          16.77,
          167.54,
          22.89,
          188.7,
          16.42,
          169.14,
          22.3,
          186.9,
          16.41,
          168.52,
          22.18,
          184.35,
          16.28,
          166.01,
          21.85,
          175.97,
          16.58,
          154,
          22.25,
          171.71,
          17.1,
          152.01,
          22.99,
          168.38,
          16.99,
          160.16,
          22.65,
          168.22,
          16.86,
          167.96,
          22.27,
          171.19,
          16.83,
          168.18,
          22.38,
          171.5,
          16.39,
          168.99,
          22.01,
          172.58,
          15.83,
          168.3,
          20.88,
          169.05,
          16.3,
          165.2,
          21.3,
          170.31,
          16.65,
          162.46,
          21.33,
          172.97,
          16.85,
          161.07,
          21.42,
          171.93,
          17.2,
          163.1,
          22.06,
          176.5,
          17.35,
          162.07,
          21.8,
          178,
          17.33,
          160.6,
          22.15,
          176.6,
          16.5,
          162.09,
          21.51,
          178.15,
          16.52,
          168.66,
          21.3,
          185.13,
          16.22,
          166.4,
          20.51,
          184.51,
          16.33,
          168.7,
          20.66,
          184.49,
          16.98,
          171.5,
          21.24,
          184.58,
          17,
          176.16,
          21.37,
          190.48,
          17.41,
          171.51,
          21.4,
          191.65,
          17.19,
          174.65,
          20.63,
          188.75,
          17.2,
          172.79,
          20.4,
          189.13,
          17.12,
          178.8,
          19.96,
          198.44,
          17.15,
          179.94,
          20.07,
          203.98,
          16.66,
          189.8,
          19.13,
          205.7,
          16.48,
          192.28,
          19.04,
          208.31,
          16.82,
          198.39,
          19.05,
          205.04,
          16.75,
          190.2,
          18.92,
          206.45,
          17.17,
          194.08,
          19.04,
          209,
          17.22,
          192.49,
          18.85,
          213,
          17.31,
          199.85,
          19.07,
          214.24,
          17.32,
          198.85,
          19.26,
          210.42,
          17.39,
          202.94,
          19.32,
          212.41,
          17.59,
          203.7,
          19.53,
          209.95,
          17.52,
          205.04,
          19.52,
          197.51,
          17.57,
          209,
          19.48,
          199,
          17.72,
          217.21,
          19.5,
          203.9,
          17.55,
          207.88,
          19.12,
          202.47,
          17.58,
          214,
          19.21,
          204.5,
          18.27,
          209.79,
          19.59,
          208,
          18.38,
          211.93,
          19.42,
          208.39,
          18.7,
          210.81,
          19.43,
          208.17,
          18.47,
          205.69,
          19.45,
          195.19,
          18.46,
          201.3,
          19.22,
          198.11,
          18.1,
          189.16,
          18.52,
          194.19,
          17.65,
          194.01,
          18.3,
          199,
          17.59,
          191.49,
          18.37,
          195.52,
          17.64,
          193.29,
          18.12,
          194.4,
          17.44,
          200.25,
          18.01,
          198.65,
          17.51,
          198,
          18.07,
          191.37,
          17.58,
          202.68,
          18.2,
          191.08,
          17.68,
          204.04,
          18.27,
          193.07,
          17.87,
          206.02,
          18.31,
          196.03,
          17.88,
          204.38,
          18.13,
          196.98,
          18.12,
          210.9,
          18.2,
          197.21,
          18.15,
          209.39,
          18.5,
          198.37,
          17.8,
          210,
          18.3,
          195.05,
          18.11,
          217,
          18.51,
          190.29,
          18.22,
          222.15,
          18.6,
          192.24,
          18.43,
          211.3,
          18.83,
          188.89,
          18.27,
          208.31,
          18.84,
          190.2,
          18.35,
          214.4,
          18.89,
          199.9,
          17.4,
          215.28,
          18.19,
          207.29,
          17.53,
          224.61,
          18.31,
          200.01,
          17.42,
          222.9,
          18.07,
          199,
          17.64,
          223.71,
          18.14,
          205.49,
          17.87,
          235,
          18.23,
          205,
          18.03,
          245.01,
          18.29,
          200.29,
          18.18,
          234.78,
          18.02,
          195.91,
          19.39,
          230,
          18.97,
          198.3,
          19.5,
          226.99,
          18.63,
          201,
          19.72,
          224.63,
          19.05,
          210.65,
          19.92,
          219.72,
          19.08,
          191.5,
          20.05,
          219.72,
          19.38,
          189.74,
          20.12,
          229.99,
          19.65,
          194.14,
          20.04,
          227.35,
          19.87,
          191.47,
          20,
          233.27,
          19.78,
          192.33,
          19.24,
          224.66,
          19.15,
          188.59,
          19.57,
          218.71,
          19.23,
          188.95,
          19.29,
          218.71,
          19.24,
          189,
          19.66,
          221.8,
          19.22,
          189.77,
          19.21,
          224.37,
          19.09,
          180.4,
          19.58,
          224.99,
          19.21,
          184.74,
          19.35,
          209,
          19.34,
          181.98,
          19.4,
          201.8,
          19.36,
          178.46,
          18.64,
          205.83,
          18.68,
          178.65,
          17.93,
          204,
          18.3,
          173.5,
          18.15,
          200.02,
          18.51,
          178.16,
          17.97,
          199.3,
          18.46,
          176,
          17.57,
          202.8,
          18.1,
          183.45,
          17.35,
          211,
          17.92,
          181.98,
          17.68,
          196.34,
          18.22,
          172.11,
          17.98,
          185.39,
          18.12,
          178.51,
          18.53,
          180.99,
          18.68,
          178.39,
          19.08,
          183.57,
          18.6,
          170,
          19.52,
          179.63,
          18.85,
          172.8,
          19.37,
          188.73,
          19.16,
          173.37,
          20.21,
          190.98,
          20.09,
          175.15,
          20.57,
          196.01,
          19.74,
          175.59,
          19,
          199.01,
          19.23,
          168.64,
          19.23,
          196.08,
          19.43,
          168,
          19.24,
          202.4,
          19.9,
          175,
          18.45,
          206.86,
          19.36,
          164.2,
          18.04,
          198.69,
          19.44,
          157.78,
          18.4,
          197.53,
          19.43,
          154.16,
          17.88,
          204.02,
          19.16,
          158.31,
          17.8,
          201.73,
          18.5,
          157.19,
          17.72,
          205,
          18.1,
          156.68,
          18.39,
          197.52,
          18.01,
          153.68,
          18.46,
          200.5,
          18.13,
          157.2,
          19.16,
          207.01,
          18.37,
          163,
          19.36,
          202.49,
          18.59,
          170,
          19.3,
          204.43,
          18.29,
          172.3,
          19.42,
          191.1,
          18.26,
          176.88,
          20.34,
          200.61,
          18.55,
          182.85,
          20.62,
          202.08,
          19.19,
          179.29,
          19.67,
          205.9,
          18.39,
          179,
          19.95,
          218.76,
          18.63,
          177.96,
          19.89,
          217.5,
          18.63,
          175.59,
          19.89,
          229.8,
          18.61,
          176.5,
          19.81,
          221.01,
          18.74,
          178.29,
          19.73,
          218.77,
          18.66,
          179.72,
          19.06,
          206.39,
          18.22,
          180,
          17.86,
          207.26,
          17.55,
          180,
          17.68,
          211.21,
          17.41,
          183.5,
          17.81,
          215.85,
          17.4,
          182,
          17.89,
          214.07,
          17.64,
          177.3,
          18.01,
          204.1,
          17.9,
          166.85,
          17.69,
          206.45,
          17.67,
          161.1,
          17.96,
          223.73,
          17.94,
          157.2,
          17.75,
          220.51,
          18.33,
          160.37,
          17.76,
          212,
          17.98,
          162.59,
          18.8,
          217.01,
          18.7,
          168.12,
          20.1,
          245.76,
          19.79,
          172.21,
          20.38,
          265.67,
          19.91,
          179.03,
          20.45,
          267.5,
          19.73,
          182.15,
          20.6,
          259.82,
          20.03,
          178.73,
          21.21,
          258,
          20.41,
          178.52,
          21.34,
          259.29,
          20.45,
          180.13,
          21.62,
          271.12,
          20.8,
          183.5,
          20.76,
          270.05,
          19.82,
          186.25,
          21.12,
          261.11,
          20.41,
          183.96,
          21.16,
          263.9,
          20.51,
          183.99,
          21.27,
          261.99,
          20.31,
          174.11,
          21.51,
          272.12,
          20.7,
          176,
          22.55,
          284.5,
          21.34,
          183,
          22.78,
          267.1,
          21.47,
          180.66,
          22.06,
          274.99,
          21.04,
          195.8,
          21.81,
          260.48,
          20.79,
          195.44,
          23.2,
          280,
          21.31,
          204,
          22.62,
          271.69,
          20.55,
          196.71,
          22.34,
          271.29,
          19.68,
          199.87,
          22.78,
          271.18,
          20.23,
          193.3,
          23.36,
          270.2,
          20.72,
          186.61,
          23.08,
          253.34,
          20.44,
          183,
          23.14,
          270.8,
          20.22,
          182.75,
          22.97,
          273.55,
          20.02,
          186,
          22.16,
          263.73,
          19.89,
          184.3,
          22.65,
          257.98,
          20.16,
          177.2,
          23.1,
          249,
          20.8,
          173.66,
          23.26,
          241.81,
          20.89,
          175.7,
          23.22,
          245.99,
          20.92,
          174,
          23.37,
          243.89,
          21.56,
          173.36,
          24.2,
          245,
          22.03,
          175.28,
          24.65,
          233.1,
          21.96,
          171.99,
          24.53,
          242.21,
          22.16,
          177.49,
          24.3,
          242.16,
          22.28,
          176.5,
          24.54,
          254.64,
          22.39,
          172.99,
          23.77,
          246.5,
          22.1,
          164.62,
          23.89,
          245.44,
          22.36,
          166.31,
          23.92,
          269.4,
          22.6,
          172.1,
          24.2,
          273.1,
          23.11,
          170.99,
          24.5,
          268.39,
          23.27,
          167.47,
          24.79,
          278,
          23.02,
          168.48,
          25.01,
          275.59,
          23.48,
          165.58,
          24.6,
          281.5,
          23.14,
          165.66,
          23.48,
          262.6,
          21.65,
          163,
          23.49,
          260.21,
          21.41,
          158.8,
          23.82,
          271.1,
          21.61,
          172.04,
          23.6,
          269.99,
          21.56,
          172.19,
          23.9,
          269.48,
          22.57,
          174.8,
          23.6,
          264,
          22.81,
          180.09,
          23.32,
          235.71,
          22.97,
          175.25,
          23.07,
          237.69,
          22.58,
          172.41,
          23.55,
          233.9,
          22.54,
          174.18,
          23.53,
          226.3,
          22.35,
          173.7,
          23.86,
          216.6,
          22,
          176.98,
          24.05,
          214,
          22.65,
          172.2,
          23.5,
          226.48,
          22.26,
          179.31,
          23.29,
          242.89,
          21.74,
          182.75,
          23.59,
          224.1,
          22.21,
          179.54,
          23.35,
          228.79,
          21.5,
          180.5,
          22.94,
          225.08,
          21.74,
          179.99,
          22.94,
          215,
          21.55,
          176.63,
          23.29,
          223.99,
          21.87,
          178.2,
          22.98,
          228.48,
          21.61,
          193.4,
          23.01,
          214.06,
          22,
          186.3,
          21.69,
          205.81,
          21.33,
          181.05,
          21.15,
          207.49,
          21.03,
          179.94,
          20.26,
          196.49,
          20.89,
          170.43,
          20.36,
          189.88,
          20.85,
          175.45,
          20.67,
          200.8,
          21.69,
          172.83,
          20.78,
          198,
          22.58,
          169.51,
          20.7,
          197.39,
          22.82,
          166.2,
          21.3,
          205.2,
          23.24,
          162.75,
          21.56,
          211.65,
          23.28,
          165.09,
          21.64,
          197,
          23.3,
          158.99,
          21.68,
          193.09,
          23.7,
          161,
          21.5,
          176.51,
          23.38,
          164.38,
          21.78,
          176,
          24.07,
          160.99,
          22.01,
          167.77,
          24.09,
          159.35,
          21.93,
          175.02,
          23.22,
          160.73,
          21.49,
          170.6,
          23.19,
          159.99,
          21.14,
          175.97,
          23.23,
          159.15,
          20.75,
          162.82,
          22.71,
          160.03,
          20.53,
          22.56,
          155.25,
          21.23,
          23.36,
          156.4,
          21.55,
          23.63,
          155.81,
          20.47,
          23.22,
          154,
          21.73,
          23.86,
          164,
          21.2,
          23.98,
          162.82,
          21.66,
          24.61,
          163.7,
          21.6,
          24.09,
          162.9,
          21.48,
          23.92,
          173.3,
          21.27,
          23.79,
          173.16,
          20.4,
          22.89,
          169.9,
          20.85,
          23.21,
          168.2,
          21.6,
          23.77,
          170.49,
          22.34,
          24.8,
          180.5,
          22.92,
          25.3,
          174.55,
          23.01,
          25.97,
          185.33,
          21.65,
          24.87,
          180,
          21.45,
          24.91,
          173.65,
          21.38,
          24.68,
          168,
          22.09,
          26.21,
          175.14,
          22.16,
          25.71,
          175.75,
          21.93,
          26.41,
          180.03,
          22.38,
          25.81,
          180.06,
          23.85,
          26.75,
          186.01,
          24.3,
          27.73,
          190.9,
          23.82,
          26.64,
          209.2,
          24.58,
          26.81,
          214.72,
          24.8,
          26.75,
          197.69,
          24.93,
          25.76,
          194.7,
          24.6,
          24.77,
          197.92,
          24.95,
          24.69,
          193.82,
          23.28,
          23.59,
          191.5,
          24.55,
          24.07,
          184.68,
          23.09,
          23.1,
          186,
          22.81,
          23.2,
          182.24,
          23.08,
          23.45,
          184.41,
          22.37,
          22.85,
          181.6,
          22.49,
          22.91,
          195.2,
          22.03,
          22.93,
          192.9,
          22.23,
          23.41,
          190.14,
          22.47,
          23.22,
          188.79,
          22.34,
          23.88,
          184.9,
          22.7,
          24.21,
          189.95,
          21,
          22.71,
          189.26,
          20.17,
          21.35,
          193.84,
          20.7,
          21.1,
          203.17,
          21,
          20.85,
          205.9,
          20.38,
          20.51,
          205.43,
          19.85,
          20.32,
          207.2,
          19.9,
          20.1,
          203.99,
          19.56,
          19.73,
          199,
          18.17,
          18.86,
          197.95,
          18.6,
          19.65,
          179.95,
          19.34,
          20.87,
          178,
          19.2,
          20.9,
          172.37,
          19.17,
          20.63,
          165.1,
          18.85,
          20.49,
          162.73,
          18.04,
          20.21,
          168.36,
          18.26,
          20.17,
          164.5,
          18,
          19.82,
          168.8,
          17.9,
          19.81,
          170.2,
          18.28,
          20.14,
          176.37,
          18.36,
          20.46,
          176.3,
          18.95,
          20.77,
          180,
          19.01,
          20.42,
          166.85,
          18.74,
          20.08,
          164.43,
          18.88,
          20.53,
          163.7,
          18.52,
          20.12,
          162,
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          225,
          19.35,
          191.97,
          20.45,
          228.88,
          19.76,
          196.88,
          20.2,
          231.6,
          19.76,
          194.78,
          20.59,
          233.99,
          20.12,
          193.99,
          20.06,
          236,
          19.86,
          195,
          19.78,
          228.8,
          19.51,
          192,
          19.68,
          223.8,
          19.48,
          191,
          19.55,
          221.77,
          19.47,
          193.99,
          19.9,
          231,
          19.48,
          189.98,
          19.92,
          227,
          19.47,
          183.59,
          19.91,
          229,
          19.46,
          187,
          19.71,
          216.95,
          19.65,
          184,
          19.74,
          210,
          19.5,
          181.98,
          18.89,
          211.02,
          18.74,
          184.75,
          18.2,
          206.88,
          18.54,
          180,
          18.44,
          205,
          18.75,
          179.25,
          18.17,
          210,
          18.57,
          182,
          17.68,
          216.01,
          18.15,
          185,
          17.68,
          219.9,
          18.3,
          182,
          18.32,
          197.99,
          18.53,
          180,
          18.05,
          190.7,
          18.32,
          181.18,
          19,
          192.4,
          18.82,
          178.5,
          19.5,
          187.39,
          18.97,
          174.88,
          20.99,
          188.8,
          19.23,
          174.3,
          20.27,
          193.85,
          20.2,
          177.89,
          20.51,
          198,
          20.15,
          178.5,
          20.77,
          201.28,
          20.23,
          179.5,
          19.15,
          200.5,
          19.38,
          171.77,
          19.55,
          202.4,
          20.08,
          176,
          19.56,
          207.5,
          19.93,
          175,
          18.6,
          210.96,
          19.67,
          165,
          18.5,
          202.95,
          19.78,
          159.18,
          18.78,
          206,
          19.62,
          160.6,
          17.92,
          206.99,
          19.35,
          159.36,
          17.93,
          207.87,
          18.6,
          158.5,
          18.29,
          205.93,
          18.46,
          156.89,
          18.88,
          205.45,
          18.29,
          159.91,
          19.26,
          212.39,
          18.38,
          162.51,
          19.47,
          208.34,
          18.75,
          168.52,
          19.97,
          208.05,
          18.68,
          173,
          20.05,
          205.6,
          18.48,
          174.2,
          20.07,
          198,
          18.8,
          182.85,
          20.62,
          207.8,
          19.25,
          184.42,
          21.2,
          210.16,
          19.41,
          183,
          20.22,
          216.7,
          18.84,
          183.99,
          20.2,
          225.4,
          18.89,
          181.88,
          19.92,
          233.76,
          18.7,
          178.3,
          20.3,
          237.32,
          18.87,
          180.33,
          20.58,
          244,
          19.33,
          181.16,
          19.98,
          219.09,
          18.78,
          181.4,
          19.25,
          208.98,
          18.59,
          185,
          17.9,
          213.98,
          17.64,
          183.91,
          18.05,
          218,
          17.61,
          186,
          18.07,
          218.5,
          17.72,
          182.6,
          18.15,
          217.55,
          17.88,
          178,
          18.14,
          208.88,
          18.04,
          168.16,
          18.05,
          220.28,
          17.94,
          161.63,
          18.24,
          229,
          18.48,
          166,
          18.07,
          224.9,
          18.46,
          163.21,
          18.99,
          222.99,
          18.87,
          170.8,
          19.99,
          244.8,
          19.65,
          172,
          20.38,
          265.66,
          19.93,
          180.04,
          20.66,
          272.18,
          20.27,
          183.33,
          20.8,
          272.72,
          20.2,
          183.4,
          21.2,
          263.82,
          20.4,
          180.61,
          21.43,
          263.4,
          20.5,
          183.35,
          21.82,
          271.9,
          20.92,
          183,
          21.72,
          273.43,
          20.85,
          186.67,
          21,
          277.02,
          20.39,
          190.38,
          21.34,
          268.38,
          20.7,
          185,
          21.5,
          268.88,
          20.96,
          184.47,
          21.63,
          274.67,
          20.91,
          175,
          22.78,
          289.35,
          21.62,
          184.39,
          23.05,
          287.4,
          21.8,
          189.1,
          23,
          281.88,
          21.66,
          196.4,
          22.15,
          281.01,
          21.08,
          197.15,
          22.74,
          276,
          21.1,
          204.77,
          23.5,
          284.87,
          21.65,
          209.84,
          22.93,
          275.68,
          20.79,
          200.62,
          22.79,
          275,
          19.8,
          200.88,
          23.39,
          278.5,
          20.78,
          193.5,
          23.6,
          272.38,
          20.82,
          186.99,
          23.12,
          270.3,
          20.44,
          185.98,
          23.41,
          276.5,
          20.31,
          187.32,
          23.09,
          274.8,
          20.28,
          186.79,
          22.67,
          273.5,
          20.18,
          184.68,
          23.28,
          259.87,
          20.72,
          177.54,
          23.69,
          254.98,
          21.05,
          175.49,
          23.4,
          252,
          21.13,
          177.5,
          23.43,
          252.7,
          21.38,
          179.5,
          24.35,
          246.5,
          22.11,
          177.16,
          25.06,
          245.01,
          22.27,
          178.77,
          24.88,
          244.99,
          22.3,
          178.85,
          24.57,
          249.85,
          22.49,
          181.26,
          24.48,
          250.27,
          22.39,
          177.96,
          24.96,
          258.18,
          22.73,
          174.8,
          24.14,
          254.67,
          22.77,
          167.88,
          23.92,
          268,
          22.65,
          172.7,
          24.25,
          279.7,
          22.95,
          173.5,
          24.53,
          274.88,
          23.26,
          170.99,
          24.71,
          281.6,
          23.44,
          170.6,
          25.16,
          283.45,
          23.43,
          169.88,
          25.12,
          282.37,
          23.77,
          167.49,
          24.73,
          285,
          23.17,
          166.7,
          23.6,
          267.2,
          21.88,
          163,
          24.09,
          274.9,
          21.71,
          171.95,
          23.84,
          275.38,
          21.62,
          174.5,
          24.13,
          279.6,
          22.52,
          174.81,
          24.11,
          271.78,
          22.86,
          180.6,
          23.82,
          268.65,
          23.39,
          183,
          23.43,
          241.68,
          23.03,
          176.22,
          23.59,
          245.81,
          22.86,
          174,
          23.64,
          239.85,
          23.35,
          174.88,
          23.75,
          228.98,
          22.61,
          177.99,
          24.04,
          219.89,
          22.68,
          180.76,
          24.3,
          228,
          23.05,
          182.65,
          23.7,
          250,
          22.55,
          183.75,
          23.49,
          248,
          22.5,
          183.45,
          23.71,
          230.6,
          22.22,
          182.51,
          23.45,
          232.17,
          21.84,
          182.3,
          23.19,
          225.11,
          21.92,
          181.66,
          24.23,
          232.2,
          22.39,
          179.98,
          23.65,
          227.99,
          21.94,
          184.93,
          23.23,
          236.88,
          22.15,
          194.5,
          23.19,
          218,
          22.22,
          186.5,
          21.95,
          216.36,
          21.71,
          185.98,
          21.24,
          208.22,
          21.11,
          180,
          20.43,
          198.25,
          20.96,
          177,
          20.77,
          203.5,
          21.79,
          178.9,
          20.94,
          207.77,
          22.68,
          174.63,
          20.88,
          201.6,
          23.09,
          173.49,
          21.51,
          211.55,
          23.23,
          166.99,
          21.72,
          216.66,
          23.52,
          167.77,
          21.73,
          218.88,
          23.44,
          168.13,
          21.93,
          202.57,
          23.74,
          161.46,
          22.09,
          197.48,
          23.77,
          166.08,
          21.73,
          181.81,
          24.2,
          167.58,
          22.11,
          179.77,
          24.3,
          161.17,
          22.07,
          181.78,
          24.42,
          161.98,
          21.93,
          187.5,
          23.45,
          164.91,
          21.65,
          175.2,
          23.43,
          163.3,
          21.4,
          182.36,
          23.35,
          161.99,
          20.83,
          180,
          23.09,
          161.55,
          21.28,
          23.44,
          158.87,
          21.65,
          23.85,
          158.43,
          21.55,
          24.07,
          156.5,
          21.65,
          24.02,
          162.9,
          21.73,
          24.31,
          167.26,
          21.55,
          24.77,
          164.79,
          22.11,
          25.08,
          168.5,
          22.12,
          24.8,
          173.3,
          21.7,
          24.26,
          174.86,
          21.38,
          24.31,
          175.1,
          20.91,
          23.5,
          174.5,
          21.92,
          24.13,
          173.5,
          22.69,
          24.97,
          181.92,
          22.98,
          25.25,
          180.99,
          23.49,
          26.1,
          182.98,
          23.08,
          26.13,
          187.85,
          22.15,
          25.35,
          181.68,
          21.68,
          25.28,
          173.65,
          21.96,
          26.22,
          172.86,
          22.56,
          26.67,
          178.6,
          22.28,
          26.3,
          181.99,
          22.68,
          26.89,
          182.84,
          23.8,
          26.6,
          187.24,
          24.19,
          27.99,
          192.18,
          24.55,
          28.07,
          214,
          24.96,
          27.55,
          214,
          24.84,
          27,
          217.46,
          24.9,
          27.05,
          198.58,
          25.31,
          25.93,
          199.8,
          25.24,
          25.32,
          202.89,
          25.18,
          24.99,
          195.98,
          24.1,
          24.28,
          191.88,
          24.99,
          24.3,
          188.99,
          23.54,
          23.7,
          191.28,
          23.18,
          23.36,
          187.99,
          23.47,
          23.85,
          186.66,
          23.32,
          23.27,
          196.16,
          22.6,
          23.14,
          198,
          22.23,
          23.33,
          199.98,
          22.8,
          23.8,
          194.66,
          22.97,
          24.23,
          190,
          22.84,
          24.24,
          191.49,
          22.78,
          24.38,
          192.3,
          21.95,
          23.35,
          192.77,
          20.89,
          21.69,
          203.01,
          21.01,
          21.24,
          210.1,
          21,
          20.93,
          207,
          20.64,
          20.97,
          210.86,
          20.1,
          20.49,
          209.5,
          19.98,
          20.2,
          205.55,
          19.56,
          19.73,
          203.52,
          18.48,
          19.54,
          197.95,
          19.1,
          20.3,
          181.87,
          19.58,
          21.25,
          178.49,
          19.2,
          20.9,
          172.72,
          19.3,
          20.91,
          167.2,
          18.86,
          20.69,
          168.36,
          18.26,
          20.36,
          168.66,
          18.49,
          20.37,
          170.63,
          18.07,
          20.02,
          172.88,
          18.65,
          20.41,
          177,
          18.64,
          20.4,
          178.5,
          19.1,
          20.79,
          179.58,
          19.07,
          20.79,
          180,
          19.1,
          20.6,
          168.64,
          18.85,
          20.51,
          165.49,
          19.06,
          20.66,
          165.2,
          18.8,
          20.62,
          169,
          18.88,
          20.63,
          168.1,
          19.05,
          20.81,
          169.15,
          19.03,
          20.79,
          170.83,
          19.39,
          21.23,
          167.69,
          19.47,
          21.21,
          167.79,
          19.86,
          21.61,
          167,
          20.06,
          21.69,
          166.55,
          20.51,
          21.79,
          164.24,
          20.88,
          22.07,
          157.78,
          20,
          20.65,
          158.88,
          19.61,
          20.42,
          161,
          19.7,
          20.35,
          168.83,
          19.68,
          20.25,
          174,
          19.62,
          20.02,
          175.47,
          18.99,
          19.21,
          179,
          18.98,
          19.45,
          174.99,
          18.5,
          19.38,
          171,
          17.93,
          18,
          172.41,
          17.43,
          17.88,
          179.2,
          17.47,
          18.3,
          181.08,
          17.94,
          18.68,
          181,
          18.3,
          18.83,
          184.44,
          18.5,
          18.76,
          183.47,
          18,
          18.29,
          186.27,
          17.75,
          18.11,
          188.91,
          18.5,
          18.58,
          182.8,
          18.48,
          18.45,
          177.49,
          18.34,
          18.45,
          175.57,
          18.05,
          18.07,
          177,
          18.36,
          18.23,
          180.27,
          17.93,
          18,
          179.5,
          17.9,
          18,
          172.15,
          18,
          17.95,
          156.98,
          18.29,
          18.45,
          151.88,
          18.78,
          18.75,
          163.9,
          18.5,
          18.59,
          164.5,
          18,
          18.2,
          167.9,
          17.6,
          17.88,
          162.38,
          18.1,
          18.35,
          160,
          17.37,
          17.8,
          160.1,
          16.92,
          17.3,
          161.68,
          16.12,
          16.89,
          164.1,
          16.11,
          16.87,
          159.98,
          16.05,
          16.77,
          156,
          15.55,
          16.43,
          148.26,
          15.27,
          16.29,
          148.84,
          15.41,
          16.08,
          140.4,
          15.37,
          16.05,
          142.58,
          15.31,
          16.05,
          140.99,
          15.61,
          16.14,
          142,
          15.83,
          16.32,
          142.2,
          15.84,
          16.47,
          143.31,
          16.05,
          16.53,
          146.46,
          16.09,
          16.4,
          144.41,
          15.72,
          16.19,
          144.7,
          15.54,
          16.22,
          148.6,
          15.48,
          16.21,
          148.1,
          15.3,
          16.22,
          152.99,
          15.3,
          16.29,
          152.48,
          15.48,
          16.46,
          157.76,
          15.56,
          16.6,
          162,
          15.43,
          16.61,
          173,
          15.24,
          16.38,
          181.77,
          15.06,
          16.28,
          185.19,
          15.33,
          16.52,
          192.95,
          15.53,
          16.45,
          184.4,
          15.23,
          16.54,
          181.78,
          15.68,
          16.63,
          181.6,
          15.18,
          15.94,
          175.48,
          14.46,
          15.74,
          173.21,
          14.61,
          15.88,
          181.98,
          14.69,
          15.95,
          186.86,
          14.71,
          15.99,
          179.72,
          14.71,
          15.97,
          176.9,
          15.14,
          16,
          174.6,
          15.35,
          16.26,
          177.91,
          15.3,
          16.48,
          180.32,
          15.35,
          16.71,
          178.94,
          14.51,
          16.11,
          178.8,
          14.46,
          16.13,
          178,
          14.5,
          16.02,
          178.48,
          14.66,
          16.19,
          182.9,
          14.02,
          15.91,
          187.63,
          13.9,
          16,
          189.8,
          13.96,
          16.08,
          191.4,
          13.85,
          16.08,
          191.1,
          14.15,
          16.31,
          189,
          13.62,
          15.83,
          193.48,
          13.53,
          15.9,
          188.9,
          13.51,
          15.85,
          182,
          13.63,
          15.88,
          183.88,
          13.43,
          15.73,
          172.88,
          13.68,
          15.88,
          168.89,
          13.99,
          15.98,
          175.86,
          14.29,
          16.23,
          176.6,
          14.65,
          16.55,
          175,
          14.68,
          16.51,
          171.99,
          14.77,
          16.56,
          165.43,
          14.28,
          16.35,
          164.6,
          14.55,
          16.58,
          169.99,
          14.86,
          16.78,
          173.73,
          15.19,
          17.6,
          173.99,
          15.08,
          17.8,
          175,
          15.48,
          18.3,
          174.54,
          15.66,
          18.58,
          170,
          16,
          18.87,
          167,
          16.63,
          19.34,
          169.4,
          15.68,
          18.67,
          171.51,
          14.32,
          17,
          175.69,
          13.49,
          16.55,
          184.2,
          13.15,
          16.21,
          183.93,
          12.88,
          15.9,
          181.49,
          12.97,
          16.01,
          177.88,
          12.88,
          15.87,
          177.18,
          12.69,
          15.69,
          175.13,
          12.76,
          15.79,
          174.95,
          12.84,
          15.79,
          174.28,
          12.8,
          15.79,
          170.5,
          12.92,
          15.77,
          174.84,
          12.99,
          15.81,
          175.66,
          12.97,
          15.8,
          168,
          13.02,
          15.97,
          166.16,
          13.39,
          16.05,
          164.45,
          13.71,
          16.25,
          161,
          13.73,
          16.27,
          158.1,
          13.85,
          16.35,
          167.53,
          13.62,
          16.31,
          166.93,
          13.64,
          16.44,
          162.2,
          13.88,
          16.72,
          160.25,
          13.63,
          16.44,
          160.7,
          13.39,
          16.3,
          164.55,
          13.04,
          16.11,
          163.6,
          13.18,
          16.29,
          161.8,
          13.19,
          16.09,
          167,
          13.07,
          15.91,
          163.77,
          12.98,
          15.95,
          155.6,
          13.34,
          16.13,
          155.21,
          13.57,
          16.34,
          152.08,
          13.62,
          16.24,
          166,
          13.45,
          16.31,
          168.88,
          13.34,
          16.15,
          166,
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          183.21,
          15.79,
          165.6,
          22,
          184.91,
          15.9,
          168.5,
          22,
          181.59,
          15.99,
          163.06,
          22.39,
          181.89,
          16.13,
          169,
          22.52,
          183.67,
          16.3,
          164.2,
          23.08,
          184.03,
          16.31,
          166,
          22.82,
          185.98,
          16.66,
          167.38,
          22.8,
          188.63,
          16.32,
          168,
          22.31,
          186,
          16.39,
          168.41,
          22.19,
          186.57,
          16.35,
          166.88,
          21.9,
          176.48,
          16.5,
          153.74,
          22.13,
          171.21,
          17.1,
          150,
          22.95,
          167.5,
          17.03,
          158.89,
          22.78,
          168.8,
          16.77,
          166.83,
          22.22,
          171.24,
          16.92,
          168.17,
          22.43,
          171.85,
          16.3,
          167.5,
          22.05,
          172.58,
          16.02,
          171.49,
          21.35,
          171.37,
          16.39,
          168.52,
          21.45,
          174.9,
          16.5,
          163.33,
          21.32,
          173.37,
          16.95,
          160.53,
          21.51,
          172.34,
          17.08,
          160.91,
          22,
          176,
          17.34,
          160.76,
          21.8,
          178.93,
          17.45,
          159.92,
          22,
          176.65,
          16.47,
          162.48,
          21.49,
          179.4,
          16.54,
          167.01,
          21.3,
          184.02,
          16.25,
          166.99,
          20.52,
          184.65,
          16.35,
          167.01,
          20.73,
          183.36,
          17,
          171.1,
          21.17,
          184.17,
          16.91,
          174.99,
          21.4,
          190.5,
          17.41,
          171.5,
          21.45,
          192.8,
          17.26,
          173.7,
          21,
          189,
          17.29,
          173.49,
          20.59,
          188.01,
          17.1,
          180.98,
          20.1,
          197,
          17.11,
          180.11,
          20.16,
          203.71,
          16.58,
          190.11,
          19.15,
          205.03,
          16.48,
          191.5,
          19.07,
          208.06,
          16.86,
          199.38,
          19.11,
          206,
          16.76,
          189.19,
          18.94,
          205.69,
          17.16,
          194,
          19.13,
          211,
          17.22,
          191.88,
          18.92,
          213,
          17.33,
          201.29,
          19.01,
          214.3,
          17.33,
          199.99,
          19.36,
          210.38,
          17.4,
          202.44,
          19.45,
          210,
          17.62,
          206.06,
          19.66,
          210.94,
          17.49,
          207.56,
          19.5,
          197.17,
          17.56,
          206.99,
          19.41,
          197.12,
          17.7,
          216.06,
          19.5,
          203.45,
          17.55,
          209,
          19.24,
          204.5,
          17.51,
          215.98,
          19.1,
          205.32,
          18.16,
          209.78,
          19.51,
          209.47,
          18.47,
          211.93,
          19.54,
          210.97,
          18.51,
          209,
          19.3,
          205.26,
          18.49,
          205,
          19.45,
          196.48,
          18.45,
          201.77,
          19.12,
          195,
          18.3,
          193.4,
          18.74,
          194.97,
          17.85,
          193.21,
          18.35,
          199.15,
          17.64,
          190.7,
          18.35,
          196.32,
          17.62,
          194,
          18.09,
          194.52,
          17.43,
          200.23,
          18,
          199.81,
          17.54,
          199.5,
          18.06,
          191.37,
          17.41,
          201.02,
          18.09,
          188.75,
          17.62,
          204.04,
          18.19,
          192.57,
          17.74,
          205.23,
          18.25,
          198,
          17.77,
          202.22,
          18.14,
          195.58,
          18.11,
          210,
          18.2,
          197.11,
          18.03,
          209.96,
          18.53,
          199,
          17.8,
          209,
          18.28,
          194.65,
          18.09,
          215.78,
          18.48,
          191.99,
          18.15,
          221,
          18.45,
          191,
          18.36,
          213.2,
          18.74,
          188.3,
          18.35,
          209.5,
          18.87,
          190.5,
          18.31,
          215.51,
          18.9,
          198.81,
          17.35,
          214.88,
          18.11,
          207.19,
          17.46,
          222.68,
          18.25,
          199.53,
          17.48,
          222.58,
          18.19,
          200,
          17.62,
          221.11,
          18.14,
          203,
          17.85,
          235.1,
          18.04,
          205,
          18.08,
          241.8,
          18.22,
          200.99,
          18.1,
          235.58,
          18.03,
          195.36,
          19.37,
          229,
          18.97,
          196.8,
          19.33,
          226.99,
          18.67,
          202.98,
          19.63,
          225.37,
          19.34,
          213.77,
          19.8,
          217.11,
          19.01,
          195,
          20,
          218.66,
          19.3,
          190.01,
          20.06,
          227.07,
          19.58,
          192.9,
          19.71,
          228,
          19.58,
          190,
          20.16,
          231.8,
          19.9,
          193.99,
          19.52,
          226,
          19.3,
          190,
          19.75,
          217.43,
          19.3,
          188.75,
          19.15,
          219.73,
          19.2,
          187.5,
          19.45,
          220,
          19.34,
          189.99,
          19,
          223.7,
          19.14,
          182.02,
          19.59,
          220.5,
          19.24,
          183.59,
          19.3,
          209.48,
          19.33,
          181.5,
          19.3,
          199.82,
          19.33,
          178.52,
          19,
          203.01,
          19,
          177.95,
          18.17,
          207,
          18.41,
          172.19,
          18.09,
          199,
          18.54,
          177.56,
          17.86,
          197.91,
          18.27,
          173.8,
          17.47,
          203.31,
          18.1,
          182,
          17.21,
          208.21,
          17.92,
          180.24,
          17.6,
          195.01,
          18.19,
          172.65,
          18.1,
          185.76,
          18.25,
          179.93,
          17.87,
          178,
          18,
          175,
          18.9,
          181.31,
          18.6,
          171.98,
          19.3,
          181.7,
          18.75,
          172.7,
          19.31,
          188.13,
          19.08,
          172.81,
          20.18,
          190.3,
          20.09,
          175.15,
          20.36,
          195.5,
          19.74,
          176.3,
          18.89,
          199.01,
          19.26,
          169.05,
          19.11,
          195.13,
          19.31,
          167.31,
          19.24,
          202.38,
          19.83,
          173.22,
          18.6,
          206.88,
          19.42,
          162.2,
          17.93,
          194.94,
          19.35,
          157.85,
          18.5,
          195.79,
          19.35,
          153,
          18,
          202,
          19.27,
          158.5,
          17.48,
          201.57,
          18.41,
          155.01,
          17.72,
          204.39,
          18.15,
          156.3,
          18.21,
          190,
          18.14,
          153.66,
          18.56,
          200.51,
          18.1,
          159,
          19.2,
          208.07,
          18.35,
          162,
          19.42,
          206.25,
          18.58,
          167,
          19.35,
          207.02,
          18.29,
          170,
          19.5,
          188.55,
          18.25,
          169.98,
          19.97,
          197.66,
          18.68,
          182.85,
          20.48,
          203.9,
          19.1,
          181.9,
          19.67,
          208.98,
          18.43,
          178,
          19.85,
          215.73,
          18.62,
          179.84,
          20.09,
          219.9,
          18.77,
          174,
          19.78,
          227.99,
          18.62,
          174.01,
          19.79,
          217.39,
          18.72,
          178.83,
          19.99,
          232.5,
          18.68,
          178.58,
          19.09,
          206.47,
          18.19,
          180,
          17.91,
          203.17,
          17.58,
          179.99,
          17.55,
          211,
          17.32,
          182.8,
          17.7,
          212.98,
          17.36,
          186,
          18.04,
          212.76,
          17.7,
          177.13,
          17.99,
          204.55,
          17.72,
          166.95,
          17.64,
          200,
          17.55,
          160.01,
          17.92,
          218,
          17.85,
          158.5,
          17.91,
          226.34,
          18.48,
          163.82,
          17.85,
          210,
          18.09,
          159.58,
          18.87,
          217.21,
          18.77,
          165,
          19.96,
          237.99,
          19.62,
          170.5,
          20.38,
          264.01,
          19.92,
          180.04,
          20.45,
          269.26,
          19.7,
          183.01,
          20.58,
          259.31,
          20.06,
          178,
          21.1,
          256,
          20.28,
          178.24,
          21.36,
          256.87,
          20.44,
          177.88,
          21.41,
          271.5,
          20.67,
          182.66,
          20.72,
          269.78,
          19.81,
          186.13,
          21,
          258.5,
          20.39,
          182.09,
          21.15,
          262.77,
          20.55,
          183.99,
          21.5,
          262,
          20.6,
          174.15,
          21.3,
          271.25,
          20.68,
          174.59,
          22.78,
          288,
          21.54,
          183.71,
          22.68,
          261.02,
          21.45,
          180.6,
          22.08,
          275.99,
          21.06,
          195.12,
          21.69,
          259,
          20.94,
          194,
          22.72,
          275.2,
          21.09,
          203.94,
          22.81,
          269.54,
          20.83,
          199,
          22.36,
          272.7,
          19.9,
          199.9,
          22.58,
          271,
          19.54,
          193.3,
          23.36,
          273.81,
          20.72,
          187.79,
          23.08,
          254.77,
          20.49,
          184.38,
          23.03,
          269.6,
          20.26,
          182.74,
          22.85,
          270.2,
          20.06,
          187.25,
          22.38,
          263.77,
          19.99,
          184.5,
          22.6,
          256,
          20.1,
          178,
          23.13,
          253.99,
          20.7,
          175,
          23.02,
          241.82,
          20.9,
          174,
          23.22,
          247.5,
          20.85,
          173.93,
          23.35,
          243.37,
          21.35,
          173.85,
          24.31,
          244.01,
          22.05,
          175.3,
          24.66,
          232,
          22.09,
          172.39,
          24.43,
          241.02,
          22.1,
          177,
          24.15,
          241.01,
          22.29,
          179,
          24.48,
          250.03,
          22.39,
          174.8,
          23.99,
          245.02,
          22.13,
          165,
          23.72,
          243.48,
          22.42,
          165.39,
          23.89,
          268,
          22.63,
          172.7,
          24.2,
          276.88,
          22.9,
          171.97,
          24.33,
          268.4,
          23.22,
          165.81,
          24.68,
          278,
          23.06,
          168.48,
          24.91,
          274.35,
          23.43,
          166.02,
          24.64,
          280.9,
          23.35,
          165.66,
          23.54,
          263.12,
          21.63,
          162.73,
          23.48,
          264.12,
          21.37,
          158,
          23.88,
          273,
          21.64,
          170.33,
          23.41,
          267.03,
          21.39,
          171.78,
          23.82,
          268,
          22.5,
          174.8,
          23.85,
          261.21,
          22.78,
          179.43,
          23.15,
          233.2,
          22.87,
          175,
          23.14,
          240.58,
          22.71,
          172.36,
          23.52,
          230.45,
          22.51,
          173.84,
          23.29,
          222.25,
          22.31,
          173.09,
          23.62,
          215,
          21.9,
          177.33,
          24,
          211.52,
          22.68,
          173,
          23.67,
          227.1,
          22.49,
          179.21,
          23.1,
          244,
          21.55,
          182.75,
          23.35,
          226.37,
          22.5,
          180,
          23.34,
          227.02,
          21.52,
          181,
          23.29,
          220,
          21.76,
          180,
          22.95,
          213.01,
          21.5,
          176.63,
          23.87,
          223.2,
          22.16,
          177.62,
          23.32,
          224.45,
          21.51,
          184.93,
          23.19,
          214.06,
          22.05,
          187.35,
          22.3,
          205.1,
          21.37,
          181.8,
          21.08,
          206.47,
          21,
          180.68,
          20.03,
          195.18,
          20.9,
          171,
          20.36,
          188.4,
          20.95,
          177,
          20.76,
          198,
          21.79,
          172.94,
          20.79,
          196,
          22.68,
          170.54,
          20.72,
          195,
          23.02,
          165,
          21.51,
          203.3,
          23.05,
          163,
          21.71,
          208,
          23.36,
          164.51,
          21.46,
          197,
          23.25,
          158.99,
          21.88,
          190.08,
          23.68,
          161.45,
          21.55,
          176.99,
          23.49,
          166.08,
          21.7,
          172.99,
          24.19,
          161.05,
          22.08,
          164.98,
          24.25,
          159,
          21.77,
          175.02,
          23.45,
          161.28,
          21.38,
          166,
          23.28,
          159.99,
          21.28,
          170,
          23.21,
          158,
          20.84,
          172,
          22.94,
          159.88,
          20.58,
          176.11,
          22.99,
          153.42,
          21.21,
          23.36,
          156,
          21.57,
          23.55,
          155,
          20.55,
          23.49,
          153,
          21.53,
          23.9,
          159.98,
          21.3,
          24.06,
          162.79,
          21.45,
          24.54,
          160.59,
          21.6,
          24.26,
          168.5,
          21.4,
          24,
          173.3,
          21.37,
          23.98,
          173.17,
          20.58,
          23.38,
          170,
          20.84,
          23.5,
          171.78,
          21.68,
          23.77,
          168.9,
          22.44,
          24.86,
          181,
          22.7,
          24.97,
          171.01,
          22.77,
          25.69,
          182,
          21.58,
          25.07,
          182,
          21.62,
          25.11,
          175.02,
          21.54,
          25,
          169,
          21.6,
          25.8,
          172,
          22.4,
          26.05,
          178.6,
          22.27,
          26.13,
          180.5,
          22.38,
          25.81,
          178.12,
          23.8,
          26.5,
          186,
          23.96,
          27.6,
          187.44,
          24,
          26.9,
          212.42,
          24.6,
          26.94,
          214,
          24.79,
          26.67,
          200,
          24.9,
          25.7,
          194.82,
          24.6,
          25.03,
          199.32,
          24.18,
          24.45,
          193.9,
          23.48,
          23.62,
          191.4,
          23.38,
          23.88,
          185,
          23,
          22.98,
          186,
          22.81,
          23.5,
          183,
          22.78,
          23.12,
          184,
          22.31,
          22.99,
          184,
          22.3,
          22.96,
          195.23,
          21.72,
          22.84,
          192.9,
          22.23,
          23.33,
          192,
          22.5,
          23.45,
          190,
          22.15,
          23.88,
          185.27,
          22.51,
          23.9,
          191.04,
          21.2,
          22.7,
          189.39,
          21,
          22.5,
          191.71,
          20.68,
          21.15,
          203.01,
          21,
          20.76,
          208.99,
          20.39,
          20.67,
          198,
          20,
          20.58,
          205.8,
          19.9,
          20.28,
          204.99,
          19.52,
          19.8,
          201.34,
          18.08,
          18.87,
          200,
          18.4,
          19.52,
          180.5,
          19.1,
          20.3,
          179,
          19.21,
          20.97,
          171.9,
          19,
          20.56,
          165.5,
          18.87,
          20.54,
          162.68,
          18.02,
          20.06,
          168.36,
          18.26,
          20.25,
          163.1,
          18.15,
          20,
          168.74,
          17.96,
          19.79,
          171.5,
          18.36,
          20.15,
          176,
          18.3,
          20.4,
          175.7,
          19,
          20.63,
          179,
          18.91,
          20.52,
          168,
          18.8,
          20.16,
          165,
          18.8,
          20.51,
          163.7,
          18.6,
          20.48,
          163.82,
          18.71,
          20.43,
          166.7,
          18.73,
          20.43,
          165.98,
          18.79,
          20.46,
          168.84,
          18.92,
          20.5,
          163.78,
          19.31,
          21.12,
          166.05,
          19.47,
          21.19,
          164.74,
          19.78,
          21.33,
          162.59,
          19.93,
          21.46,
          164.58,
          19.7,
          21.26,
          157.1,
          19.9,
          20.89,
          156.4,
          20,
          20.47,
          157.67,
          19.05,
          19.92,
          160.16,
          19.48,
          20.3,
          168.83,
          19.62,
          19.94,
          172.48,
          18.85,
          19,
          173.9,
          18.83,
          18.85,
          174.5,
          18.59,
          19.18,
          169.18,
          17.78,
          18.02,
          168.5,
          17.38,
          17.73,
          172.41,
          17.08,
          17.86,
          178.92,
          17.42,
          18.3,
          177.99,
          17.81,
          18.59,
          179.8,
          18.2,
          18.52,
          182,
          18,
          18.28,
          183,
          17.67,
          18.17,
          184,
          17.71,
          18.1,
          188.91,
          18.37,
          18.55,
          178.05,
          18.35,
          18.2,
          173.98,
          17.71,
          18.05,
          174.81,
          17.65,
          17.9,
          174.1,
          17.74,
          17.9,
          180.15,
          17.54,
          17.35,
          172.66,
          17.76,
          17.81,
          162.25,
          18,
          17.79,
          151,
          18.2,
          18.36,
          151.5,
          17.53,
          18.19,
          162.63,
          17.94,
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objs as go\n",
    "# Construct a 2 x 1 Plotly figure\n",
    "fig = make_subplots(rows=2, cols=1)\n",
    "trade_date = data['trade_date'].apply(lambda x: pd.to_datetime(str(x), format='%Y%m%d'))\n",
    "# price Line\n",
    "fig.append_trace(\n",
    "    go.Scatter(\n",
    "        x=trade_date,\n",
    "        y=data['open'],\n",
    "        line=dict(color='#ff9900', width=1),\n",
    "        name='open',\n",
    "        # showlegend=False,\n",
    "        legendgroup='1',\n",
    "    ), row=1, col=1\n",
    ")\n",
    "# Candlestick chart for pricing\n",
    "fig.append_trace(\n",
    "    go.Candlestick(\n",
    "        x=trade_date,\n",
    "        open=df['open'],\n",
    "        high=df['high'],\n",
    "        low=df['low'],\n",
    "        close=df['close'],\n",
    "        increasing_line_color='#ff9900',\n",
    "        decreasing_line_color='black',\n",
    "        showlegend=False\n",
    "    ), row=1, col=1\n",
    ")\n",
    "# Fast Signal (%k)\n",
    "fig.append_trace(\n",
    "    go.Scatter(\n",
    "        x=trade_date,\n",
    "        y=data['MACD_12_26_9'],\n",
    "        line=dict(color='#ff9900', width=2),\n",
    "        name='macd',\n",
    "        # showlegend=False,\n",
    "        legendgroup='2',\n",
    "    ), row=2, col=1\n",
    ")\n",
    "# Slow signal (%d)\n",
    "fig.append_trace(\n",
    "    go.Scatter(\n",
    "        x=trade_date,\n",
    "        y=data['MACDs_12_26_9'],\n",
    "        line=dict(color='#000000', width=2),\n",
    "        # showlegend=False,\n",
    "        legendgroup='2',\n",
    "        name='signal'\n",
    "    ), row=2, col=1\n",
    ")\n",
    "# Colorize the histogram values\n",
    "colors = np.where(data['MACDh_12_26_9'] < 0, '#000', '#ff9900')\n",
    "# Plot the histogram\n",
    "fig.append_trace(\n",
    "    go.Bar(\n",
    "        x=trade_date,\n",
    "        y=data['MACDh_12_26_9'],\n",
    "        name='histogram',\n",
    "        marker_color=colors,\n",
    "    ), row=2, col=1\n",
    ")\n",
    "# Make it pretty\n",
    "layout = go.Layout(\n",
    "    plot_bgcolor='#efefef',\n",
    "    # Font Families\n",
    "    font_family='Monospace',\n",
    "    font_color='#000000',\n",
    "    font_size=20,\n",
    "    xaxis=dict(\n",
    "        rangeslider=dict(\n",
    "            visible=False\n",
    "        )\n",
    "    )\n",
    ")\n",
    "# Update options and show plot\n",
    "fig.update_layout(layout)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fca2421d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d5b2ab2148>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "dif,dea,bar=data.MACD_12_26_9,data.MACDs_12_26_9,data.MACDh_12_26_9\n",
    "kl_index=trade_date\n",
    "plt.figure(figsize=(14,7))\n",
    "#plt.plot(kl_index,data.open ,label='open')\n",
    "plt.plot(kl_index,dif)\n",
    "plt.plot(kl_index,dea)\n",
    "bar_red=np.where(bar>0,bar,0)\n",
    "bar_green=np.where(bar<0,bar,0)\n",
    "plt.bar(kl_index,bar_red, facecolor='r')\n",
    "plt.bar(kl_index,bar_green, facecolor='g')\n",
    "plt.legend(['macd dif','singal dea','hist bar','hist bar'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "64adb3da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001D5BF6113C8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5BF443108>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5BF47A648>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "atr14=ta.atr(data.high,data.low,data.close)\n",
    "\n",
    "atr21=ta.atr(data.high,data.low,data.close,length=21)\n",
    "atr14\n",
    "#df.index=trade_date\n",
    "#df.plot(subplots=True, figsize=(18, 9), grid=True, xlabel=\"Date\")\n",
    "pd.DataFrame({'close':data.close, 'art14':atr14, 'atr21':atr21}).set_index(trade_date).plot(\n",
    "    subplots=True, figsize=(18, 9), grid=True, xlabel=\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "984616c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th>market</th>\n",
       "      <th>exchange</th>\n",
       "      <th>list_status</th>\n",
       "      <th>list_date</th>\n",
       "      <th>delist_date</th>\n",
       "      <th>is_hs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001</td>\n",
       "      <td>平安银行</td>\n",
       "      <td>深圳</td>\n",
       "      <td>银行</td>\n",
       "      <td>主板</td>\n",
       "      <td>SZSE</td>\n",
       "      <td>L</td>\n",
       "      <td>19910403</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002</td>\n",
       "      <td>万科A</td>\n",
       "      <td>深圳</td>\n",
       "      <td>全国地产</td>\n",
       "      <td>主板</td>\n",
       "      <td>SZSE</td>\n",
       "      <td>L</td>\n",
       "      <td>19910129</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004</td>\n",
       "      <td>国华网安</td>\n",
       "      <td>深圳</td>\n",
       "      <td>软件服务</td>\n",
       "      <td>主板</td>\n",
       "      <td>SZSE</td>\n",
       "      <td>L</td>\n",
       "      <td>19910114</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>000005</td>\n",
       "      <td>ST星源</td>\n",
       "      <td>深圳</td>\n",
       "      <td>环境保护</td>\n",
       "      <td>主板</td>\n",
       "      <td>SZSE</td>\n",
       "      <td>L</td>\n",
       "      <td>19901210</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>000006</td>\n",
       "      <td>深振业A</td>\n",
       "      <td>深圳</td>\n",
       "      <td>区域地产</td>\n",
       "      <td>主板</td>\n",
       "      <td>SZSE</td>\n",
       "      <td>L</td>\n",
       "      <td>19920427</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4796</th>\n",
       "      <td>871857.BJ</td>\n",
       "      <td>871857</td>\n",
       "      <td>泓禧科技</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>北交所</td>\n",
       "      <td>BSE</td>\n",
       "      <td>L</td>\n",
       "      <td>20220228</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4797</th>\n",
       "      <td>871981.BJ</td>\n",
       "      <td>871981</td>\n",
       "      <td>晶赛科技</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>北交所</td>\n",
       "      <td>BSE</td>\n",
       "      <td>L</td>\n",
       "      <td>20211115</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4798</th>\n",
       "      <td>872925.BJ</td>\n",
       "      <td>872925</td>\n",
       "      <td>锦好医疗</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>北交所</td>\n",
       "      <td>BSE</td>\n",
       "      <td>L</td>\n",
       "      <td>20211025</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4799</th>\n",
       "      <td>873169.BJ</td>\n",
       "      <td>873169</td>\n",
       "      <td>七丰精工</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>北交所</td>\n",
       "      <td>BSE</td>\n",
       "      <td>L</td>\n",
       "      <td>20220415</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4800</th>\n",
       "      <td>689009.SH</td>\n",
       "      <td>689009</td>\n",
       "      <td>九号公司-WD</td>\n",
       "      <td>北京</td>\n",
       "      <td>摩托车</td>\n",
       "      <td>CDR</td>\n",
       "      <td>SSE</td>\n",
       "      <td>L</td>\n",
       "      <td>20201029</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4801 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ts_code  symbol     name  area industry market exchange list_status  \\\n",
       "0     000001.SZ  000001     平安银行    深圳       银行     主板     SZSE           L   \n",
       "1     000002.SZ  000002      万科A    深圳     全国地产     主板     SZSE           L   \n",
       "2     000004.SZ  000004     国华网安    深圳     软件服务     主板     SZSE           L   \n",
       "3     000005.SZ  000005     ST星源    深圳     环境保护     主板     SZSE           L   \n",
       "4     000006.SZ  000006     深振业A    深圳     区域地产     主板     SZSE           L   \n",
       "...         ...     ...      ...   ...      ...    ...      ...         ...   \n",
       "4796  871857.BJ  871857     泓禧科技  None     None    北交所      BSE           L   \n",
       "4797  871981.BJ  871981     晶赛科技  None     None    北交所      BSE           L   \n",
       "4798  872925.BJ  872925     锦好医疗  None     None    北交所      BSE           L   \n",
       "4799  873169.BJ  873169     七丰精工  None     None    北交所      BSE           L   \n",
       "4800  689009.SH  689009  九号公司-WD    北京      摩托车    CDR      SSE           L   \n",
       "\n",
       "     list_date delist_date is_hs  \n",
       "0     19910403        None     S  \n",
       "1     19910129        None     S  \n",
       "2     19910114        None     N  \n",
       "3     19901210        None     N  \n",
       "4     19920427        None     S  \n",
       "...        ...         ...   ...  \n",
       "4796  20220228        None     N  \n",
       "4797  20211115        None     N  \n",
       "4798  20211025        None     N  \n",
       "4799  20220415        None     N  \n",
       "4800  20201029        None     N  \n",
       "\n",
       "[4801 rows x 11 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pro.query('stock_basic', exchange='', list_status='L', fields='ts_code,exchange,market,symbol,name,area,industry,list_date,list_status,delist_date,is_hs')\n",
    "data.to_sql('stock_basic', engine, if_exists='replace', index=False)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "407c6cc8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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